10.2. THE ALGORITHMS

10.2.1.1. In a sufficiently complicated problem, actual sampling is better than an examination of all the chains of possibiilities.

10.2.1.2. Laplace -- when we want to know something about a complex quantity, we can estimate its value by sampling from it.

10.2.1.2.1. We picture CPUs marching through problems one step after the other in order... but in some cases randomized algorithms produce better results.

10.2.1.2.2. The key is knowing WHEN to rely on chance.

10.2.2. Metropolis Algorithm

10.2.2.1. Metropolis Algorithm: your likelihood of following a bad idea should be inversely proportional to HOW BAD an idea it is.

10.2.3. Monte Carlo Method

10.2.3.1. Excel Functions

10.2.4. Hill Climbing Algorithm

10.2.4.1. Jitter -- if it looks like you're stuck (income wise, etc.) make a few small RANDOM changes and see what happens. Then go back to Hill Climbing,

10.2.4.2. From Hill Climbing: even if you are in the habit of sometimes acting on bad idea, you should ALWAYS act on good ones.

10.3. APPLICATIONS

10.3.1. Negotiation

10.3.2. Breaking out of a Rut

11. Game Theory

11.1. THE PROBLEM

11.1.1. What's unique about litigation?

11.1.2. The Price of anarchy

11.1.2.1. measures the gap between cooperation and competition.

11.1.2.1.1. Prisoner's Dilemma

11.1.3. Idea of "Value"

11.1.3.1. It's not really what people think it's worth, but what people think OTHER people think it's worth.

11.1.4. The problem of Recursiveness

11.1.4.1. Family Feud

11.1.4.1.1. what does average opinion expect average opinion to be?

11.1.4.1.2. Anytime a person or machine simulates the working of itself or another person, it maxes itself out.

11.1.4.1.3. Recursion is theoretically infinite

11.2. THE ALGORITHMS

11.2.1. NASH Equilibrium

11.2.1.1. Nash Equilibrium always exists in 2 player games.

11.2.1.2. When we find ourselves going down rabbit hole of recursion, we step out of opponents head and look for the equilibrium, going to best strategy, assuming rational play.

11.2.1.3. Dominant Strategy

11.2.1.3.1. a strategy that avoids recursion altogether by being the best response to opponent's possible strategies regardless of what they are.

11.2.1.4. Here's the paradox - the equilibrium set for both players (both cooperating with cops) does not lead to the BEST result for both players (both keeping mouth shut).

11.2.2. Mechanism Design: Change the Game

11.2.2.1. Reverse Game Theory -- by changing consequences to worse, you can make the result better for everyone (e.g., Mafia Don tells prisoners if they cooperate with cops they die; they keep their mouth shut and both walk).

11.2.2.1.1. By reducing number of options, behavioral constraints make certain kinds of decisions less computationally challenging.

11.3. APPLICATIONS

11.3.1. Flip the game

11.3.2. Give some value to Irrationality

11.3.2.1. Lots of things override rational decisionmaking.

11.3.2.1.1. Revenge almost never works our for seeker, yet those who will respond with irrational vehemence to being taken advantage of is for that reason more likely to get a fair deal.

11.3.3. Understand (and use) herd mentality

11.3.3.1. Cascades are caused when we misinterpret what others think based on what they do.

11.3.3.2. Use value of "precedent" in cases.

12. Caching

12.1. PROBLEM

12.1.1. “It is of the highest importance not to have useless facts crowding out the useful ones.”

12.1.2. What to keep/store, and what to get rid of.

12.1.3. The way the world forgets — Ebbinghouse. Memory is not a problem of storage, but of organization. Mind has infinite amount of storage, but only a finite time to search .